# ETHOS: an Online Hate Speech Detection Dataset

arXiv: https://arxiv.org/abs/2006.08328 · HTML: https://mlsanity.com/paper/2006.08328/

## Key facts

- Cited by 66 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Jun 2020.

## Built on by (most cited first, 30 of 66)

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- [Active Learning Principles for In-Context Learning with Large Language Models](https://mlsanity.com/paper/2305.14264/) (2023)
- [From Languages to Geographies: Towards Evaluating Cultural Bias in Hate Speech Datasets](https://mlsanity.com/paper/2404.17874/) (2024)
- [Task Facet Learning: A Structured Approach to Prompt Optimization](https://mlsanity.com/paper/2406.10504/) (2024)
- [Stress-Testing Long-Context Language Models with Lifelong ICL and Task Haystack](https://mlsanity.com/paper/2407.16695/) (2024)
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- [System Prompt Optimization with Meta-Learning](https://mlsanity.com/paper/2505.09666/) (2025)
- [KOLD: Korean Offensive Language Dataset](https://mlsanity.com/paper/2205.11315/) (2022)
- [Misconfidence-based Demonstration Selection for LLM In-Context Learning](https://mlsanity.com/paper/2401.06301/) (2024)
- [MetaHate: A Dataset for Unifying Efforts on Hate Speech Detection](https://mlsanity.com/paper/2401.06526/) (2024)
- [Deep Learning for Hate Speech Detection: A Comparative Study](https://mlsanity.com/paper/2202.09517/) (2022)
- [Improving Adversarial Data Collection by Supporting Annotators: Lessons from GAHD, a German Hate Speech Dataset](https://mlsanity.com/paper/2403.19559/) (2024)
- [GAAPO: Genetic Algorithmic Applied to Prompt Optimization](https://mlsanity.com/paper/2504.07157/) (2025)
- [BanTH: A Multi-label Hate Speech Detection Dataset for Transliterated Bangla](https://mlsanity.com/paper/2410.13281/) (2024)
- [Gen-Z: Generative Zero-Shot Text Classification with Contextualized Label Descriptions](https://mlsanity.com/paper/2311.07115/) (2023)
- [Introducing MAPO: Momentum-Aided Gradient Descent Prompt Optimization](https://mlsanity.com/paper/2410.19499/) (2024)
- ["It's Not Just Hate'': A Multi-Dimensional Perspective on Detecting Harmful Speech Online](https://mlsanity.com/paper/2210.15870/) (2022)
- [An Attention Matrix for Every Decision: Faithfulness-based Arbitration Among Multiple Attention-Based Interpretations of Transformers in Text Classification](https://mlsanity.com/paper/2209.10876/) (2022)
- [Investigating Annotator Bias in Large Language Models for Hate Speech Detection](https://mlsanity.com/paper/2406.11109/) (2024)
- [Beyond Labels: Aligning Large Language Models with Human-like Reasoning](https://mlsanity.com/paper/2408.11879/) (2024)
- [MAPGD: Multi-Agent Prompt Gradient Descent for Collaborative Prompt Optimization](https://mlsanity.com/paper/2509.11361/) (2025)
- [Hypothesis Engineering for Zero-Shot Hate Speech Detection](https://mlsanity.com/paper/2210.00910/) (2022)
- [CoRAL: a Context-aware Croatian Abusive Language Dataset](https://mlsanity.com/paper/2211.06053/) (2022)
- [Quantifying Stereotypes in Language](https://mlsanity.com/paper/2401.15535/) (2024)
- [Zero-to-Strong Generalization: Eliciting Strong Capabilities of Large Language Models Iteratively without Gold Labels](https://mlsanity.com/paper/2409.12425/) (2024)
- [CoPE: A Small Language Model for Steerable and Scalable Content Labeling](https://mlsanity.com/paper/2512.18027/) (2025)

## Abstract (arXiv)

Online hate speech is a recent problem in our society that is rising at a steady pace by leveraging the vulnerabilities of the corresponding regimes that characterise most social media platforms. This phenomenon is primarily fostered by offensive comments, either during user interaction or in the form of a posted multimedia context. Nowadays, giant corporations own platforms where millions of users log in every day, and protection from exposure to similar phenomena appears to be necessary in order to comply with the corresponding legislation and maintain a high level of service quality. A robust and reliable system for detecting and preventing the uploading of relevant content will have a significant impact on our digitally interconnected society. Several aspects of our daily lives are undeniably linked to our social profiles, making us vulnerable to abusive behaviours. As a result, the lack of accurate hate speech detection mechanisms would severely degrade the overall user experience, although its erroneous operation would pose many ethical concerns. In this paper, we present 'ETHOS', a textual dataset with two variants: binary and multi-label, based on YouTube and Reddit comments validated using the Figure-Eight crowdsourcing platform. Furthermore, we present the annotation protocol used to create this dataset: an active sampling procedure for balancing our data in relation to the various aspects defined. Our key assumption is that, even gaining a small amount of labelled data from such a time-consuming process, we can guarantee hate speech occurrences in the examined material.